Overview

Dataset statistics

Number of variables14
Number of observations4096
Missing cells4096
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory448.1 KiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical3
Unsupported1

Alerts

SC_NAME has a high cardinality: 4095 distinct valuesHigh cardinality
SC_CODE is highly overall correlated with SC_GROUP and 1 other fieldsHigh correlation
OPEN is highly overall correlated with HIGH and 5 other fieldsHigh correlation
HIGH is highly overall correlated with OPEN and 5 other fieldsHigh correlation
LOW is highly overall correlated with OPEN and 5 other fieldsHigh correlation
CLOSE is highly overall correlated with OPEN and 5 other fieldsHigh correlation
LAST is highly overall correlated with OPEN and 5 other fieldsHigh correlation
PREVCLOSE is highly overall correlated with OPEN and 5 other fieldsHigh correlation
NO_TRADES is highly overall correlated with NO_OF_SHRS and 1 other fieldsHigh correlation
NO_OF_SHRS is highly overall correlated with NO_TRADES and 1 other fieldsHigh correlation
NET_TURNOV is highly overall correlated with OPEN and 7 other fieldsHigh correlation
SC_GROUP is highly overall correlated with SC_CODE and 1 other fieldsHigh correlation
SC_TYPE is highly overall correlated with SC_CODE and 1 other fieldsHigh correlation
SC_TYPE is highly imbalanced (81.4%)Imbalance
TDCLOINDI has 4096 (100.0%) missing valuesMissing
OPEN is highly skewed (γ1 = 21.16748257)Skewed
HIGH is highly skewed (γ1 = 21.16760614)Skewed
LOW is highly skewed (γ1 = 21.16892196)Skewed
CLOSE is highly skewed (γ1 = 21.1686056)Skewed
LAST is highly skewed (γ1 = 21.16875173)Skewed
PREVCLOSE is highly skewed (γ1 = 21.1341154)Skewed
SC_NAME is uniformly distributedUniform
SC_CODE has unique valuesUnique
TDCLOINDI is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-06-13 17:11:24.775349
Analysis finished2023-06-13 17:11:41.219470
Duration16.44 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

SC_CODE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551406.25
Minimum500002
Maximum974635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:41.348401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum500002
5-th percentile500411.75
Q1518015.5
median532081
Q3540006.5
95-th percentile890179.25
Maximum974635
Range474633
Interquartile range (IQR)21991

Descriptive statistics

Standard deviation95260.776
Coefficient of variation (CV)0.17275969
Kurtosis11.795543
Mean551406.25
Median Absolute Deviation (MAD)8878.5
Skewness3.6324141
Sum2.25856 × 109
Variance9.0746154 × 109
MonotonicityStrictly increasing
2023-06-13T22:41:41.514305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500002 1
 
< 0.1%
500003 1
 
< 0.1%
537292 1
 
< 0.1%
537326 1
 
< 0.1%
537392 1
 
< 0.1%
537483 1
 
< 0.1%
537524 1
 
< 0.1%
537536 1
 
< 0.1%
537573 1
 
< 0.1%
537707 1
 
< 0.1%
Other values (4086) 4086
99.8%
ValueCountFrequency (%)
500002 1
< 0.1%
500003 1
< 0.1%
500008 1
< 0.1%
500009 1
< 0.1%
500010 1
< 0.1%
500012 1
< 0.1%
500013 1
< 0.1%
500014 1
< 0.1%
500016 1
< 0.1%
500020 1
< 0.1%
ValueCountFrequency (%)
974635 1
< 0.1%
974561 1
< 0.1%
974451 1
< 0.1%
974288 1
< 0.1%
974072 1
< 0.1%
973994 1
< 0.1%
973883 1
< 0.1%
973491 1
< 0.1%
972773 1
< 0.1%
972715 1
< 0.1%

SC_NAME
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4095
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size32.1 KiB
SISL
 
2
JMJFIN
 
1
NATH BIOGEN
 
1
AGRI TECH
 
1
CHEMTECH IND
 
1
Other values (4090)
4090 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters49152
Distinct characters45
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4094 ?
Unique (%)> 99.9%

Sample

1st rowABB LTD.
2nd rowAEGIS LOGIS
3rd rowAMAR RAJA BA
4th rowA.SARABHAI
5th rowHDFC

Common Values

ValueCountFrequency (%)
SISL 2
 
< 0.1%
JMJFIN 1
 
< 0.1%
NATH BIOGEN 1
 
< 0.1%
AGRI TECH 1
 
< 0.1%
CHEMTECH IND 1
 
< 0.1%
TAAZAINT 1
 
< 0.1%
NIF100BEES 1
 
< 0.1%
VIAANINDUS 1
 
< 0.1%
DENIS CHEM 1
 
< 0.1%
POLYMAC 1
 
< 0.1%
Other values (4085) 4085
99.7%

Length

2023-06-13T22:41:41.653226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltd 129
 
2.1%
ind 87
 
1.4%
india 43
 
0.7%
fin 33
 
0.5%
i 30
 
0.5%
indus 27
 
0.4%
cap 22
 
0.4%
tech 19
 
0.3%
in 15
 
0.2%
infra 15
 
0.2%
Other values (4553) 5649
93.1%

Most occurring characters

ValueCountFrequency (%)
13494
27.5%
A 3689
 
7.5%
I 2848
 
5.8%
E 2593
 
5.3%
N 2413
 
4.9%
R 2256
 
4.6%
S 2157
 
4.4%
T 2093
 
4.3%
L 2065
 
4.2%
O 1726
 
3.5%
Other values (35) 13818
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33742
68.6%
Space Separator 13494
 
27.5%
Decimal Number 1157
 
2.4%
Other Punctuation 654
 
1.3%
Open Punctuation 47
 
0.1%
Close Punctuation 35
 
0.1%
Dash Punctuation 21
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3689
 
10.9%
I 2848
 
8.4%
E 2593
 
7.7%
N 2413
 
7.2%
R 2256
 
6.7%
S 2157
 
6.4%
T 2093
 
6.2%
L 2065
 
6.1%
O 1726
 
5.1%
C 1431
 
4.2%
Other values (16) 10471
31.0%
Decimal Number
ValueCountFrequency (%)
2 253
21.9%
0 160
13.8%
1 131
11.3%
5 120
10.4%
9 106
9.2%
8 97
 
8.4%
3 93
 
8.0%
7 73
 
6.3%
4 73
 
6.3%
6 51
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 622
95.1%
& 28
 
4.3%
' 4
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
t 1
50.0%
d 1
50.0%
Space Separator
ValueCountFrequency (%)
13494
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33744
68.7%
Common 15408
31.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3689
 
10.9%
I 2848
 
8.4%
E 2593
 
7.7%
N 2413
 
7.2%
R 2256
 
6.7%
S 2157
 
6.4%
T 2093
 
6.2%
L 2065
 
6.1%
O 1726
 
5.1%
C 1431
 
4.2%
Other values (18) 10473
31.0%
Common
ValueCountFrequency (%)
13494
87.6%
. 622
 
4.0%
2 253
 
1.6%
0 160
 
1.0%
1 131
 
0.9%
5 120
 
0.8%
9 106
 
0.7%
8 97
 
0.6%
3 93
 
0.6%
7 73
 
0.5%
Other values (7) 259
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13494
27.5%
A 3689
 
7.5%
I 2848
 
5.8%
E 2593
 
5.3%
N 2413
 
4.9%
R 2256
 
4.6%
S 2157
 
4.4%
T 2093
 
4.3%
L 2065
 
4.2%
O 1726
 
3.5%
Other values (35) 13818
28.1%

SC_GROUP
Categorical

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.1 KiB
B
1146 
X
1068 
A
708 
XT
508 
F
219 
Other values (12)
447 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8192
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowX
5th rowA

Common Values

ValueCountFrequency (%)
B 1146
28.0%
X 1068
26.1%
A 708
17.3%
XT 508
12.4%
F 219
 
5.3%
T 133
 
3.2%
M 121
 
3.0%
Z 98
 
2.4%
G 36
 
0.9%
E 20
 
0.5%
Other values (7) 39
 
1.0%

Length

2023-06-13T22:41:41.771790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b 1146
28.0%
x 1068
26.1%
a 708
17.3%
xt 508
12.4%
f 219
 
5.3%
t 133
 
3.2%
m 121
 
3.0%
z 98
 
2.4%
g 36
 
0.9%
e 20
 
0.5%
Other values (7) 39
 
1.0%

Most occurring characters

ValueCountFrequency (%)
3571
43.6%
X 1576
19.2%
B 1146
 
14.0%
A 708
 
8.6%
T 647
 
7.9%
F 225
 
2.7%
M 131
 
1.6%
Z 99
 
1.2%
G 36
 
0.4%
E 20
 
0.2%
Other values (5) 33
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4621
56.4%
Space Separator 3571
43.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 1576
34.1%
B 1146
24.8%
A 708
15.3%
T 647
14.0%
F 225
 
4.9%
M 131
 
2.8%
Z 99
 
2.1%
G 36
 
0.8%
E 20
 
0.4%
P 20
 
0.4%
Other values (4) 13
 
0.3%
Space Separator
ValueCountFrequency (%)
3571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4621
56.4%
Common 3571
43.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 1576
34.1%
B 1146
24.8%
A 708
15.3%
T 647
14.0%
F 225
 
4.9%
M 131
 
2.8%
Z 99
 
2.1%
G 36
 
0.8%
E 20
 
0.4%
P 20
 
0.4%
Other values (4) 13
 
0.3%
Common
ValueCountFrequency (%)
3571
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3571
43.6%
X 1576
19.2%
B 1146
 
14.0%
A 708
 
8.6%
T 647
 
7.9%
F 225
 
2.7%
M 131
 
1.6%
Z 99
 
1.2%
G 36
 
0.4%
E 20
 
0.2%
Other values (5) 33
 
0.4%

SC_TYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.1 KiB
Q
3858 
D
 
170
B
 
67
P
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4096
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowQ
2nd rowQ
3rd rowQ
4th rowQ
5th rowQ

Common Values

ValueCountFrequency (%)
Q 3858
94.2%
D 170
 
4.2%
B 67
 
1.6%
P 1
 
< 0.1%

Length

2023-06-13T22:41:41.880736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T22:41:42.026197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
q 3858
94.2%
d 170
 
4.2%
b 67
 
1.6%
p 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Q 3858
94.2%
D 170
 
4.2%
B 67
 
1.6%
P 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4096
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 3858
94.2%
D 170
 
4.2%
B 67
 
1.6%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 3858
94.2%
D 170
 
4.2%
B 67
 
1.6%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q 3858
94.2%
D 170
 
4.2%
B 67
 
1.6%
P 1
 
< 0.1%

OPEN
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3295
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2882.0202
Minimum0.05
Maximum1083851.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:42.163215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile2.4325
Q119.69
median79.95
Q3369.825
95-th percentile2002.7625
Maximum1083851.2
Range1083851.1
Interquartile range (IQR)350.135

Descriptive statistics

Standard deviation47962.682
Coefficient of variation (CV)16.642035
Kurtosis452.30459
Mean2882.0202
Median Absolute Deviation (MAD)73.375
Skewness21.167483
Sum11804755
Variance2.3004188 × 109
MonotonicityNot monotonic
2023-06-13T22:41:42.330224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 7
 
0.2%
30 6
 
0.1%
25 6
 
0.1%
7.15 6
 
0.1%
4 6
 
0.1%
1000 6
 
0.1%
31 5
 
0.1%
71 5
 
0.1%
2 5
 
0.1%
16 5
 
0.1%
Other values (3285) 4039
98.6%
ValueCountFrequency (%)
0.05 1
< 0.1%
0.22 1
< 0.1%
0.25 1
< 0.1%
0.32 1
< 0.1%
0.34 1
< 0.1%
0.35 1
< 0.1%
0.36 1
< 0.1%
0.38 1
< 0.1%
0.4 1
< 0.1%
0.41 2
< 0.1%
ValueCountFrequency (%)
1083851.18 1
< 0.1%
1060000 1
< 0.1%
1059990 1
< 0.1%
1048788.99 1
< 0.1%
1046500 1
< 0.1%
1038500 1
< 0.1%
1020000 1
< 0.1%
1015530.68 1
< 0.1%
762000 1
< 0.1%
199001 1
< 0.1%

HIGH
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3358
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2889.8444
Minimum0.05
Maximum1083851.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:42.495120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile2.4975
Q120
median81.145
Q3376.65
95-th percentile2048.5
Maximum1083851.2
Range1083851.1
Interquartile range (IQR)356.65

Descriptive statistics

Standard deviation47985.597
Coefficient of variation (CV)16.604907
Kurtosis452.32801
Mean2889.8444
Median Absolute Deviation (MAD)74.665
Skewness21.167606
Sum11836802
Variance2.3026175 × 109
MonotonicityNot monotonic
2023-06-13T22:41:42.653027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960 6
 
0.1%
28.5 6
 
0.1%
105 6
 
0.1%
31 6
 
0.1%
41 6
 
0.1%
965 6
 
0.1%
39 5
 
0.1%
7.15 5
 
0.1%
34.5 5
 
0.1%
7.25 5
 
0.1%
Other values (3348) 4040
98.6%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.23 1
 
< 0.1%
0.27 1
 
< 0.1%
0.32 1
 
< 0.1%
0.35 2
< 0.1%
0.36 1
 
< 0.1%
0.39 1
 
< 0.1%
0.41 3
0.1%
0.44 1
 
< 0.1%
0.45 1
 
< 0.1%
ValueCountFrequency (%)
1083851.18 1
< 0.1%
1063170 1
< 0.1%
1060000 1
< 0.1%
1048788.99 1
< 0.1%
1047500 1
< 0.1%
1038500 1
< 0.1%
1020000 1
< 0.1%
1015530.68 1
< 0.1%
762000 1
< 0.1%
199001 1
< 0.1%

LOW
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3331
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2876.0244
Minimum0.05
Maximum1083851.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:42.812937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile2.3725
Q119.0975
median77.505
Q3362.8125
95-th percentile1997
Maximum1083851.2
Range1083851.1
Interquartile range (IQR)343.715

Descriptive statistics

Standard deviation47961.801
Coefficient of variation (CV)16.676424
Kurtosis452.34839
Mean2876.0244
Median Absolute Deviation (MAD)71.38
Skewness21.168922
Sum11780196
Variance2.3003343 × 109
MonotonicityNot monotonic
2023-06-13T22:41:42.984926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 7
 
0.2%
38 6
 
0.1%
960 6
 
0.1%
99 6
 
0.1%
1 6
 
0.1%
19 6
 
0.1%
107 5
 
0.1%
0.66 5
 
0.1%
16 5
 
0.1%
3.8 5
 
0.1%
Other values (3321) 4039
98.6%
ValueCountFrequency (%)
0.05 1
< 0.1%
0.21 1
< 0.1%
0.25 1
< 0.1%
0.31 1
< 0.1%
0.33 1
< 0.1%
0.34 1
< 0.1%
0.35 1
< 0.1%
0.37 1
< 0.1%
0.39 1
< 0.1%
0.4 2
< 0.1%
ValueCountFrequency (%)
1083851.18 1
< 0.1%
1060000 1
< 0.1%
1059990 1
< 0.1%
1048788.99 1
< 0.1%
1046500 1
< 0.1%
1038500 1
< 0.1%
1020000 1
< 0.1%
1015530.68 1
< 0.1%
762000 1
< 0.1%
199001 1
< 0.1%

CLOSE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3552
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2883.5231
Minimum0.05
Maximum1083851.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:43.167454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile2.445
Q119.55
median78.99
Q3370.1
95-th percentile2006.7875
Maximum1083851.2
Range1083851.1
Interquartile range (IQR)350.55

Descriptive statistics

Standard deviation47985.103
Coefficient of variation (CV)16.641137
Kurtosis452.35782
Mean2883.5231
Median Absolute Deviation (MAD)72.775
Skewness21.168606
Sum11810911
Variance2.3025701 × 109
MonotonicityNot monotonic
2023-06-13T22:41:43.343352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960 7
 
0.2%
7.15 7
 
0.2%
1000 6
 
0.1%
0.67 6
 
0.1%
1.97 5
 
0.1%
965 5
 
0.1%
3.9 5
 
0.1%
9.88 4
 
0.1%
0.66 4
 
0.1%
79 4
 
0.1%
Other values (3542) 4043
98.7%
ValueCountFrequency (%)
0.05 1
< 0.1%
0.23 1
< 0.1%
0.25 1
< 0.1%
0.31 1
< 0.1%
0.33 1
< 0.1%
0.35 1
< 0.1%
0.36 1
< 0.1%
0.38 1
< 0.1%
0.39 1
< 0.1%
0.4 2
< 0.1%
ValueCountFrequency (%)
1083851.18 1
< 0.1%
1063170 1
< 0.1%
1060000 1
< 0.1%
1048788.99 1
< 0.1%
1047500 1
< 0.1%
1038500 1
< 0.1%
1020000 1
< 0.1%
1015530.68 1
< 0.1%
762000 1
< 0.1%
199001 1
< 0.1%

LAST
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3404
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2883.486
Minimum0.05
Maximum1083851.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:43.524259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile2.43
Q119.53
median79
Q3370.1625
95-th percentile2011.75
Maximum1083851.2
Range1083851.1
Interquartile range (IQR)350.6325

Descriptive statistics

Standard deviation47984.985
Coefficient of variation (CV)16.64131
Kurtosis452.36233
Mean2883.486
Median Absolute Deviation (MAD)72.8
Skewness21.168752
Sum11810759
Variance2.3025588 × 109
MonotonicityNot monotonic
2023-06-13T22:41:43.697151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960 7
 
0.2%
1000 7
 
0.2%
0.67 7
 
0.2%
7.15 6
 
0.1%
965 6
 
0.1%
79 5
 
0.1%
23 5
 
0.1%
985 5
 
0.1%
43 5
 
0.1%
3.9 5
 
0.1%
Other values (3394) 4038
98.6%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 1
 
< 0.1%
0.31 1
 
< 0.1%
0.33 1
 
< 0.1%
0.35 1
 
< 0.1%
0.36 1
 
< 0.1%
0.39 1
 
< 0.1%
0.4 3
0.1%
0.44 1
 
< 0.1%
ValueCountFrequency (%)
1083851.18 1
< 0.1%
1063170 1
< 0.1%
1060000 1
< 0.1%
1048788.99 1
< 0.1%
1047500 1
< 0.1%
1038500 1
< 0.1%
1020000 1
< 0.1%
1015530.68 1
< 0.1%
762000 1
< 0.1%
199001 1
< 0.1%

PREVCLOSE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3547
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2886.8981
Minimum0
Maximum1088788.8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:44.068038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4075
Q119.5225
median78.785
Q3369.775
95-th percentile2006.9625
Maximum1088788.8
Range1088788.8
Interquartile range (IQR)350.2525

Descriptive statistics

Standard deviation47932.307
Coefficient of variation (CV)16.603394
Kurtosis451.22926
Mean2886.8981
Median Absolute Deviation (MAD)72.535
Skewness21.134115
Sum11824735
Variance2.297506 × 109
MonotonicityNot monotonic
2023-06-13T22:41:44.237842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 7
 
0.2%
1000 7
 
0.2%
6.05 6
 
0.1%
6.99 5
 
0.1%
5 5
 
0.1%
980 5
 
0.1%
0.64 4
 
0.1%
13.5 4
 
0.1%
3.05 4
 
0.1%
19.2 4
 
0.1%
Other values (3537) 4045
98.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.05 1
< 0.1%
0.22 1
< 0.1%
0.26 1
< 0.1%
0.32 1
< 0.1%
0.34 1
< 0.1%
0.35 1
< 0.1%
0.36 1
< 0.1%
0.39 1
< 0.1%
0.4 1
< 0.1%
ValueCountFrequency (%)
1088788.75 1
< 0.1%
1075000 1
< 0.1%
1045000 1
< 0.1%
1038500 1
< 0.1%
1036000 1
< 0.1%
1033500 1
< 0.1%
1022500 1
< 0.1%
1022228 1
< 0.1%
762000 1
< 0.1%
199000 1
< 0.1%

NO_TRADES
Real number (ℝ)

Distinct1188
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.45874
Minimum1
Maximum33184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:44.408754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q114
median72
Q3412
95-th percentile2573.75
Maximum33184
Range33183
Interquartile range (IQR)398

Descriptive statistics

Standard deviation1714.4581
Coefficient of variation (CV)3.0106802
Kurtosis108.77825
Mean569.45874
Median Absolute Deviation (MAD)69
Skewness8.6321884
Sum2332503
Variance2939366.7
MonotonicityNot monotonic
2023-06-13T22:41:44.579646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 222
 
5.4%
2 130
 
3.2%
4 98
 
2.4%
3 97
 
2.4%
5 76
 
1.9%
6 73
 
1.8%
8 54
 
1.3%
10 54
 
1.3%
7 54
 
1.3%
9 51
 
1.2%
Other values (1178) 3187
77.8%
ValueCountFrequency (%)
1 222
5.4%
2 130
3.2%
3 97
2.4%
4 98
2.4%
5 76
 
1.9%
6 73
 
1.8%
7 54
 
1.3%
8 54
 
1.3%
9 51
 
1.2%
10 54
 
1.3%
ValueCountFrequency (%)
33184 1
< 0.1%
29540 1
< 0.1%
26487 1
< 0.1%
25145 1
< 0.1%
23716 1
< 0.1%
23536 1
< 0.1%
18359 1
< 0.1%
17938 1
< 0.1%
16361 1
< 0.1%
15381 1
< 0.1%

NO_OF_SHRS
Real number (ℝ)

Distinct3292
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103410.23
Minimum1
Maximum22741092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:44.750347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q1799
median4601.5
Q323883.75
95-th percentile295010.75
Maximum22741092
Range22741091
Interquartile range (IQR)23084.75

Descriptive statistics

Standard deviation729503.57
Coefficient of variation (CV)7.054462
Kurtosis437.71194
Mean103410.23
Median Absolute Deviation (MAD)4488.5
Skewness18.508952
Sum4.2356832 × 108
Variance5.3217546 × 1011
MonotonicityNot monotonic
2023-06-13T22:41:44.966765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 45
 
1.1%
1 45
 
1.1%
10 25
 
0.6%
2 22
 
0.5%
20 18
 
0.4%
30 13
 
0.3%
200 13
 
0.3%
50 13
 
0.3%
4 11
 
0.3%
1000 10
 
0.2%
Other values (3282) 3881
94.8%
ValueCountFrequency (%)
1 45
1.1%
2 22
0.5%
3 9
 
0.2%
4 11
 
0.3%
5 7
 
0.2%
6 7
 
0.2%
7 7
 
0.2%
8 5
 
0.1%
9 8
 
0.2%
10 25
0.6%
ValueCountFrequency (%)
22741092 1
< 0.1%
17467118 1
< 0.1%
16903226 1
< 0.1%
13915063 1
< 0.1%
10670045 1
< 0.1%
10610614 1
< 0.1%
8625950 1
< 0.1%
6653621 1
< 0.1%
6618267 1
< 0.1%
6108617 1
< 0.1%

NET_TURNOV
Real number (ℝ)

Distinct4059
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8329660.2
Minimum0
Maximum8.8470292 × 108
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-06-13T22:41:45.185641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1289
Q146379
median345358.5
Q32837729.8
95-th percentile35577451
Maximum8.8470292 × 108
Range8.8470292 × 108
Interquartile range (IQR)2791350.8

Descriptive statistics

Standard deviation37002314
Coefficient of variation (CV)4.4422357
Kurtosis196.5878
Mean8329660.2
Median Absolute Deviation (MAD)340955
Skewness11.761079
Sum3.4118288 × 1010
Variance1.3691712 × 1015
MonotonicityNot monotonic
2023-06-13T22:41:45.372534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990 3
 
0.1%
13 3
 
0.1%
96 3
 
0.1%
33 3
 
0.1%
28800 3
 
0.1%
71523 2
 
< 0.1%
32637 2
 
< 0.1%
2235 2
 
< 0.1%
9108 2
 
< 0.1%
547 2
 
< 0.1%
Other values (4049) 4071
99.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
13 3
0.1%
ValueCountFrequency (%)
884702924 1
< 0.1%
807061674 1
< 0.1%
659846757 1
< 0.1%
510385371 1
< 0.1%
480202286 1
< 0.1%
478258514 1
< 0.1%
449039150 1
< 0.1%
442159023 1
< 0.1%
437944624 1
< 0.1%
351172214 1
< 0.1%

TDCLOINDI
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4096
Missing (%)100.0%
Memory size32.1 KiB

Interactions

2023-06-13T22:41:39.396027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:26.844094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.227654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.574883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.930862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.444186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.779422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.134647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.516865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.902066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:39.524951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:26.996359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.360587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.707808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:31.062940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.567118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.913346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.295559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.648783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.032998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:39.659875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.132281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.490506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.837733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:31.200861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.702038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.048270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.428478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.787701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.161917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:39.790799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.262208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.623428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.975654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:31.331786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.843962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.186199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.564400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.927621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.299836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:39.925723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.404135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.762352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.113576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:31.468706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.975884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.319112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.697325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.067543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.433575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:40.057648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.561034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.894275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.253496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:31.603630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.114801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.452039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.835247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.210459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.563501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:40.188571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.693959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.029197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.392416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:31.741550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.244728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.588959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.970170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.354377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.697426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:40.320496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.829885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.166117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.529092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.031424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.378652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.722882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.106092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.501292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.832348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:40.454420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:27.971803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.311038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.671012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.174342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.522571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:34.859804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.247013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.635216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:38.967272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:40.584345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:28.099728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:29.441962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:30.800936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:32.306264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:33.653494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:35.000725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:36.385933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:37.772141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T22:41:39.093198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-06-13T22:41:45.507981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
SC_CODEOPENHIGHLOWCLOSELASTPREVCLOSENO_TRADESNO_OF_SHRSNET_TURNOVSC_GROUPSC_TYPE
SC_CODE1.0000.1180.1180.1180.1180.1180.116-0.133-0.071-0.0130.7000.904
OPEN0.1181.0001.0001.0001.0001.0000.9990.295-0.1640.5110.1100.149
HIGH0.1181.0001.0001.0001.0001.0000.9990.297-0.1610.5130.1100.149
LOW0.1181.0001.0001.0001.0001.0000.9990.294-0.1640.5110.1100.149
CLOSE0.1181.0001.0001.0001.0001.0000.9990.296-0.1630.5120.1100.149
LAST0.1181.0001.0001.0001.0001.0000.9990.296-0.1630.5120.1100.149
PREVCLOSE0.1160.9990.9990.9990.9990.9991.0000.296-0.1630.5120.1100.149
NO_TRADES-0.1330.2950.2970.2940.2960.2960.2961.0000.7700.8790.1120.000
NO_OF_SHRS-0.071-0.164-0.161-0.164-0.163-0.163-0.1630.7701.0000.7260.0000.000
NET_TURNOV-0.0130.5110.5130.5110.5120.5120.5120.8790.7261.0000.0960.000
SC_GROUP0.7000.1100.1100.1100.1100.1100.1100.1120.0000.0961.0000.678
SC_TYPE0.9040.1490.1490.1490.1490.1490.1490.0000.0000.0000.6781.000

Missing values

2023-06-13T22:41:40.786229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T22:41:41.070820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SC_CODESC_NAMESC_GROUPSC_TYPEOPENHIGHLOWCLOSELASTPREVCLOSENO_TRADESNO_OF_SHRSNET_TURNOVTDCLOINDI
0500002ABB LTD.AQ3936.053937.803842.753852.353852.353942.6520251048340728178NaN
1500003AEGIS LOGISAQ371.05379.50360.05363.80363.80378.0053319700635553207NaN
2500008AMAR RAJA BAAQ626.00632.05622.60624.10623.65626.0565987785505116NaN
3500009A.SARABHAIXQ22.8922.8922.1222.2622.2622.46176846801888208NaN
4500010HDFCAQ2787.952795.752773.002784.052784.052776.5033332972482883348NaN
5500012ANDHRA PETROXQ63.2164.1862.7062.9062.9063.62497530823352532NaN
6500013ANSAL INFRASTQ9.909.909.519.709.709.69411018798623NaN
7500014UTIQUEXQ5.525.605.305.525.505.25197105306578015NaN
8500016ARUNAHTELXTQ19.3021.2719.3019.8919.8020.2611633203685987NaN
9500020BOM DYEINGBQ83.6087.5083.3586.3585.8583.67153619150716399249NaN
SC_CODESC_NAMESC_GROUPSC_TYPEOPENHIGHLOWCLOSELASTPREVCLOSENO_TRADESNO_OF_SHRSNET_TURNOVTDCLOINDI
4086972715IFCI150212CFB12800.0012800.0012800.0012800.0012800.0012800.001338400NaN
4087972773990IFCI37DFB25500.0025500.0025500.0025500.0025500.0025500.00312306000NaN
4088973491675PCHFL31FB795.00797.00794.00796.21796.21796.531315721249711NaN
4089973883970UPCL32FB1059990.001063170.001059990.001063170.001063170.001036000.00222123160NaN
4090973994962APSBCL31FB1020000.001020000.001020000.001020000.001020000.001022228.00111020000NaN
40919740728HDFCL32FB1083851.181083851.181083851.181083851.181083851.181088788.75122167702NaN
4092974288995UPPCL32FB1060000.001060000.001060000.001060000.001060000.001075000.00111060000NaN
4093974451962APSB31FB1015530.681015530.681015530.681015530.681015530.681022500.00122031061NaN
409497456111MML26FB101685.00101750.00101685.00101728.33101715.00101650.0067712055NaN
40959746350EEL26FB102080.00102080.00102080.00102080.00102080.00102176.2011102080NaN